This invention relates generally to methods and systems for recognizing features, and, more particularly, to methods and systems for automatic detection of corners of a region.
Detection of features is a common operation in computer vision and character recognition. Specifically, the detection of corners is a necessary operation in many computer vision and character recognition problems. In one application, the recognizing of the address in the delivery items (parcels), the detection of the address label is one necessary step. When the delivery item (parcel) is imaged in order to recognize (read) the address label, the image of the label is often randomly orientated. The need to identify the four corners of the image of an address label after finding the image of the label is critical to further processing and character segmentation.
Corner detection methods, which work directly at the gray scale level, have been developed. Some of these methods compute a local measure of cornerness defined as the product of a gradient magnitude and the rate of change of gradient direction. Corner detection based on grayscale gradient or local measures of cornerness would highlight any texture areas including addresses inside the label and other noisy regions. For images of address labels, it is extremely difficult to isolate an image of an address label on parcel images due to poor contrast and a cluttered background. Traditional corner detection techniques based on grayscale gradients fail to isolate the four corners of an address label due to similar gradients existing throughout the contextual image region.
There is also a need for a method for detecting the corners of an image of an address label in order to determine the orientation.